****************************************************************************************************************************
*****************'Voters’ Moral Emotions in Response to Politicians’ Moral Violations'**************************************
*****************Annemarie Walter and Dave Redlawsk*************************************************************************
*****************Data collected by SSI 12 -20 August 2017*******************************************************************
*****************See Online Supplementary Materials (OSM) with the manuscript for the full questionnaire********************
*****************Table numbers displayed refer to the table numbers in the manuscript and OSM ******************************
*****************Dofile edited 26 July 2021*********************************************************************************
****************************************************************************************************************************

version 16
clear all
* Load dataset
use "WalterRedlawsk2021onlyselected.dta", clear


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******************recode variables for sample demographics and randomization check****************************************
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**************************************************************************************************************************

*generate treatment variable
gen treatment =0
replace treatment =1 if Q7_Page_Submit ~=.
replace treatment =2 if Q9_Page_Submit ~=.
replace treatment =3 if Q11_Page_Submit ~=.
replace treatment =4 if Q13_Page_Submit ~=.
replace treatment =5 if Q15_Page_Submit ~=.
replace treatment =6 if Q17_Page_Submit ~=.
replace treatment =7 if Q19_Page_Submit ~=.
replace treatment =8 if Q21_Page_Submit ~=.
replace treatment =9 if Q23_Page_Submit ~=.
replace treatment =10 if Q25_Page_Submit ~=.
replace treatment =11 if Q27_Page_Submit ~=.
replace treatment =12 if Q29_Page_Submit ~=.
replace treatment =13 if Q31_Page_Submit ~=.
replace treatment =14 if Q33_Page_Submit ~=.
replace treatment =15 if Q35_Page_Submit ~=.
replace treatment =1 if FL_84_DO_TreatmentHarmCareRepub ==1
replace treatment =2 if FL_84_DO_TreatmentHarmDemocrat ==1
replace treatment =3 if FL_84_DO_TreatmentHarmNonpartisa ==1
replace treatment =4 if FL_84_DO_TreatmentFairnessRepubl ==1
replace treatment =5 if FL_84_DO_TreatmentFairnessDemocr ==1
replace treatment =6 if FL_84_DO_TreatmentFairnessNonPar ==1
replace treatment =7 if FL_84_DO_TreatmentLoyaltyRepubli ==1
replace treatment =8 if FL_84_DO_TreatmentLoyaltyDemocra ==1
replace treatment =9 if FL_84_DO_TreatmentLoyaltyNonpart ==1
replace treatment =10 if FL_84_DO_TreatmentAuthorityRepub ==1
replace treatment =11 if FL_84_DO_TreatmentAuthorityDemoc ==1
replace treatment =12 if FL_84_DO_TreatmentAuthorityNonPa ==1
replace treatment =13 if FL_84_DO_TreatmentSanctityRepubl ==1
replace treatment =14 if FL_84_DO_TreatmentSanctityDemocr ==1
replace treatment =15 if FL_84_DO_TreatmentSanctityNonPar ==1

*generate party identification
gen republican =0
replace republican =1 if Q53==1
*adding lean republican
replace republican=1 if Q56==1
replace republican=1 if Q57==1

gen democrat =0
replace democrat =1 if Q53==2
*adding lean democrat
replace democrat =1 if Q56==2
replace democrat =1 if Q57==2

gen independent=0
replace independent=1 if Q57==3
gen otherparty=0
replace otherparty=1 if Q56==3
gen rest=0
replace rest =1 if independent==1
replace rest =1 if otherparty==1

*generate education variables
*Not college graduate (HS + some college + associate degree)
*Bachelors/College graduate (Bachelors' degree)
*Post-Graduate (Masters, Professional, Doctoral)

drop if Q2==.
gen nocoll=0
replace nocoll =1 if Q2==1
replace nocoll =1 if Q2==2
replace nocoll =1 if Q2==3

gen bach=0
replace bach =1 if Q2==4

gen postgrad=0
replace postgrad=1 if Q2==5
replace postgrad=1 if Q2==6
replace postgrad=1 if Q2==7

gen educ=Q2
recode educ 1=1 2=1 3=1 4=2 5=3 6=3 7=3 

*generat gender variables
gen gender=0
replace gender =1 if Q4==2
*woman =1 in new variable

*generate race variables

gen hispanic=0
replace hispanic=1 if Q5_2==1

gen other=0
replace other=1 if Q5_4==1
replace other= 1 if Q5_6==1

gen white=0
replace white =1 if Q5_1 ==1 & Q5_3~=1 & Q5_2~=1& Q5_4~=1 & Q5_5~=1 & Q5_6~=1

gen african=0
replace african=1 if Q5_3==1 & Q5_1~=1 & Q5_2~=1& Q5_4~=1 & Q5_5~=1 & Q5_6~=1 

gen asian =0
replace asian =1 if Q5_5==1 & Q5_1~=1 & Q5_2~=1& Q5_4~=1 & Q5_3~=1 & Q5_6~=1 

gen mixed =0
replace mixed =1 if Q5_1 ==1 & Q5_3==1
replace mixed =1 if Q5_1 ==1 & Q5_4==1
replace mixed =1 if Q5_1 ==1 & Q5_5==1
replace mixed =1 if Q5_1 ==1 & Q5_6==1
replace mixed =1 if Q5_3 ==1 & Q5_2==1
replace mixed =1 if Q5_4 ==1 & Q5_2==1
replace mixed =1 if Q5_6 ==1 & Q5_2==1
replace mixed =1 if Q5_5 ==1 & Q5_2==1
replace mixed =1 if Q5_4 ==1 & Q5_3==1
replace mixed =1 if Q5_5 ==1 & Q5_3==1
replace mixed =1 if Q5_6 ==1 & Q5_3==1
replace mixed =1 if Q5_5 ==1 & Q5_4==1
replace mixed =1 if Q5_6 ==1 & Q5_4==1
replace mixed =1 if Q5_6 ==1 & Q5_5==1
*for randomization check we put mixed and other together 
gen restrace=0
replace restrace =1 if mixed==1
replace restrace=1 if other==1


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*******************************************************Case selection paper***********************************************
***********************************************Deleting missings on stimulus control, dependent variables and MFQ*********
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*stimulus control
drop if Q45==.
*emotions
drop if Q37_1==.
drop if Q37_2==.
drop if Q37_3==.
drop if Q37_4==.
drop if Q37_5==.
drop if Q37_6==.
drop if Q37_7==.
drop if Q37_8==.
drop if Q37_9==.
drop if Q37_10==.
drop if Q37_11==.
drop if Q37_12==.
drop if Q37_13==.
drop if Q37_14==.
*drop morality
drop if Q59_1==.
drop if Q59_2==.
drop if Q59_3==.
drop if Q59_4==.
drop if Q59_5==.
drop if Q59_6==. 
drop if Q59_7==.
drop if Q59_8==. 
drop if Q59_9==.
drop if Q59_10==.
drop if Q59_11==. 
drop if Q59_12==. 
drop if Q59_13==.
drop if Q59_14==.
drop if Q59_15==. 
drop if Q59_16==. 
drop if Q59_17==.
drop if Q59_18==.
drop if Q59_19==. 
drop if Q59_20==.
drop if Q59_21==.
drop if Q59_22==.
drop if Q59_23==.
drop if Q58_1==.
drop if Q58_2==.
drop if Q58_3==. 
drop if Q58_4==. 
drop if Q58_5==.
drop if Q58_6==.
drop if Q58_7==. 
drop if Q58_8==. 
drop if Q58_9==.
drop if Q58_10==.
drop if Q58_11==.
drop if Q58_13==.
drop if Q58_14==.
drop if Q58_15==.
drop if Q58_16==.

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**********************************************Sample Demographics and Randomization Checks********************************
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*randomization check, displayed in Table A2.4 Randomization Checks in OSM
tab democrat treatment,chi2
tab republican treatment,chi2
tab independent treatment, chi2
anova educ treatment
tab white treatment, chi2
tab african treatment,chi2
tab hispanic treatment, chi2
tab asian treatment, chi2
tab gender treatment, chi
anova Age treatment
mlogit treatment gender asian white african hispanic republican democrat independent Age nocoll bach 

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***************************************Construct variables for analyses***************************************************
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*Create variables emotions
gen anxious= Q37_1 +1
gen optimistic= Q37_2 +1
gen contempt= Q37_3 +1
gen warmhearted= Q37_4 +1
gen hope =Q37_5 +1
gen enthusiastic=Q37_6 +1
gen shame = Q37_7 +1
gen sad = Q37_8 +1
gen admiration= Q37_9 +1
gen sympathy = Q37_10 +1
gen pride = Q37_11 +1
gen disgust = Q37_12 +1
gen uplifted = Q37_13 +1
gen angry = Q37_14 +1

*Create the moral emotion elevation, which is a combination of the seperate emotions warmhearted, uplifted and optimistic. 
*We conduct a mokken scale analysis as the variables are orinal to see if the seperate items can be combined, alpha is 0.77
*elevation, combination of warmhearted, uplifted and optimistic
msp optimistic warmhearted uplifted
gen elevation = (warmhearted+ optimistic+ uplifted)/3

*Table A2.5: Correlations between emotions in the supplementary materials   
corr angry disgust contempt anxious shame sad elevation admiration sympathy hope enthusiastic pride    

*Creating moral values variables
gen emotionally=Q58_1+1
gen treated=Q58_2+1
gen lovecountry=Q58_3+1
gen respect=Q58_4+1
gen decency=Q58_5+1
gen math =Q58_6+1
gen weak=Q58_7+1
gen unfairly=Q58_8+1
gen betray=Q58_9+1
gen traditions=Q58_10+1
gen disgusting=Q58_11+1
gen cruel=Q58_12+1
gen rights=Q58_13+1
gen loyalty=Q58_14+1
gen	chaos=Q58_15+1
gen god=Q58_16+1

gen compassion= Q59_1+1
gen fairly=Q59_2+1
gen history=Q59_3+1
gen kidrespect=Q59_4+1
gen harmlessdg=Q59_5+1
gen good=Q59_6+1
gen animal=Q59_7+1
gen justice=Q59_8+1
gen family=Q59_9+1
gen sexroles=Q59_10+1
gen unnatural=Q59_11+1
gen kill=Q59_12+1
gen rich=Q59_13+1
gen team=Q59_14+1
gen soldier=Q59_15+1
gen chastity=Q59_16+1

alpha emotionally weak cruel compassion animal kill, item /*alpha=.72*/
alpha treated unfairly rights fairly justice rich, item /*alpha=.68*/
alpha lovecountry betray loyalty history family team, item /*alpha=.72*/
alpha respect traditions chaos kidrespect sexroles soldier, item /*alpha=.69*/
alpha decency disgusting god harmlessdg unnatural chastity, item /*alpha=.79*/

*dropping respondents that made a mistake in MFQ item, which woudl improve the performance of MFQ, see MFQ theory would reduce number of observations significantly, we look how dropping them would improve the results. However, we do decide to keep these in.

gen failedmorals=0
replace failedmorals=1 if Q58_6<4
replace failedmorals=1 if Q59_6<4

/*testing what happens if exclude those who failed math and good*/
alpha emotionally weak cruel compassion animal kill if failedmorals==0, item /*alpha=.72*/
alpha treated unfairly rights fairly justice rich if failedmorals==0, item /*alpha=.67*/
alpha lovecountry betray loyalty history family team if failedmorals==0, item /*alpha=.75*/
alpha respect traditions chaos kidrespect sexroles soldier if failedmorals==0, item /*alpha=.72*/
alpha decency disgusting god harmlessdg unnatural chastity if failedmorals==0, item /*alpha=.84*/

gen care=emotionally + weak + cruel + compassion + animal+ kill
gen fairness =treated +unfairly +rights+ fairly +justice+ rich
gen ingroup =lovecountry +betray +loyalty+ history +family +team
gen authority= respect +traditions+ chaos +kidrespect+ sexroles+ soldier
gen sanctity= decency +disgusting +god +harmlessdg+ unnatural +chastity

gen ncare= (care/6)
gen nfair=(fairness/6)
gen ningroup= (ingroup/6)
gen nauthority=(authority/6)
gen nsanctity=(sanctity/6)

sum care fairness ingroup authority sanctity

*generate variables indicating exposure to moral foundation vignette violated
gen vharm=0
replace vharm=1 if treatment==1
replace vharm=1 if treatment==2
replace vharm=1 if treatment==3
gen vfair=0
replace vfair=1 if treatment==4
replace vfair=1 if treatment==5
replace vfair=1 if treatment==6
gen vloyalty=0
replace vloyalty=1 if treatment==7
replace vloyalty=1 if treatment==8
replace vloyalty=1 if treatment==9
gen vauthority=0
replace vauthority=1 if treatment==10
replace vauthority=1 if treatment==11
replace vauthority=1 if treatment==12
gen vsanctity=0
replace vsanctity=1 if treatment==13
replace vsanctity=1 if treatment==14
replace vsanctity=1 if treatment==15

*combined variable
gen mf =0
replace mf=1 if vharm==1
replace mf=2 if vfair==1
replace mf=3 if vloyalty==1
replace mf=4 if vauthority==1
replace mf=5 if vsanctity==1

*generate variables indicate partisanship actor displayed in the vignette
gen vneutral=0
replace vneutral=1 if  treatment==3
replace vneutral=1 if  treatment==6
replace vneutral=1 if  treatment==9
replace vneutral=1 if  treatment==12
replace vneutral=1 if  treatment==15

gen vrepublican=0
replace vrepublican=1 if treatment==1
replace vrepublican=1 if treatment==4
replace vrepublican=1 if treatment==7
replace vrepublican=1 if treatment==10
replace vrepublican=1 if treatment==13
gen vdemocrat=0
replace vdemocrat=1 if treatment==2
replace vdemocrat=1 if treatment==5
replace vdemocrat=1 if treatment==8
replace vdemocrat=1 if treatment==11
replace vdemocrat=1 if treatment==14
gen withpartisanlabel=0
replace withpartisanlabel=1 if treatment ==1
replace withpartisanlabel=1 if treatment ==4
replace withpartisanlabel=1 if treatment ==7
replace withpartisanlabel=1 if treatment ==10
replace withpartisanlabel=1 if treatment ==13
replace withpartisanlabel=1 if treatment ==2
replace withpartisanlabel=1 if treatment ==5
replace withpartisanlabel=1 if treatment ==8
replace withpartisanlabel=1 if treatment ==11
replace withpartisanlabel=1 if treatment ==14

gen partisanship=0
replace partisanship=1 if republican==1 & vrepublican==1
replace partisanship=1 if democrat==1 & vdemocrat==1
replace partisanship=1 if republican==0 & democrat==0 & vneutral==1 

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*create Table A1.2 and Table A2.2: Voters’ Moral Judgment of Moral Violations in % in OSM
tab Q44 if mf==1
tab Q44 if mf==2
tab Q44 if mf==3
tab Q44 if mf==4
tab Q44 if mf==5

mean Q44 if mf==1
mean Q44 if mf==2
mean Q44 if mf==3
mean Q44 if mf==4
mean Q44 if mf==5

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*Table 1 Main Manuscript**************************************************************************************************
*Table 1 Percentage of respondents reporting feeling specific moral emotions in response to exposure to one of the moral violations.  
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*These tables display the frequencies that are the basis for table 1 in the main manuscript
*The first cell of table 1 is calculated by adding category 4 and 5 of the emotion experienced, here anger (112+103) 
*Divide the number by total respondents exposed to the vignette, here harm 382 and multiply by 100= 56.28

tab angry
tab angry vharm, row
tab angry vfair
tab angry vloyalty
tab angry vauthority
tab angry vsanctity
tab disgust
tab disgust vharm
tab disgust vfair
tab disgust vloyalty
tab disgust vauthority
tab disgust vsanctity
tab shame
tab shame vharm
tab shame vfair
tab shame vloyalty
tab shame vauthority
tab shame vsanctity
tab elevation
tab elevation vharm
tab elevation vfair
tab elevation vloyalty
tab elevation vauthority
tab elevation vsanctity
tab admiration
tab admiration vharm
tab admiration vfair
tab admiration vloyalty
tab admiration vauthority
tab admiration vsanctity
tab sympathy
tab sympathy vharm
tab sympathy vfair
tab sympathy vloyalty
tab sympathy vauthority
tab sympathy vsanctity
tab pride
tab pride vharm
tab pride vfair
tab pride vloyalty
tab pride vauthority
tab pride vsanctity

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*Table A1.3: Voters’ Moral Emotional Responses to Exposure to the Meaning Components in the Vignettes in OSM**************
*Table 2 Voters’ Moral Emotional Responses to Exposure to the Meaning Components in the Vignettes in the main manuscript**
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*For Table 2 or A1.3 we constructed six new independent variables for each vignette, measuring the relative weights of the vignette’s meaning components by multiplying the proportion identifying each foundation as violated by each individuals’ perception of severity, see footnote 7 in the manuscript for more details
*An overview of the proportion identifying each foudnation as violatied is displayed in Table A1.1. in the OSM
*These new variables are called PCAREW, PFAIRW, PLOYALW, PAUTHW, PSANCW, PNONMORALW

gen PCARE=0
replace PCARE=0.773 if vharm==1
replace PCARE= 0.024 if vfair==1
replace PCARE= 0.386 if vloyalty==1
replace PCARE= 0.061  if vauthority==1
replace PCARE= 0.240 if vsanctity==1

gen PFAIR=0
replace PFAIR=0.044 if vharm==1
replace PFAIR= 0.845 if vfair==1
replace PFAIR= 0.039 if vloyalty==1
replace PFAIR= 0.020 if vauthority==1
replace PFAIR= 0.138 if vsanctity==1

gen PLOYAL=0
replace PLOYAL=0.008  if vharm==1
replace PLOYAL= 0.036 if vfair==1
replace PLOYAL= 0.425 if vloyalty==1
replace PLOYAL= 0.378 if vauthority==1
replace PLOYAL= 0.343 if vsanctity==1

gen PAUTH=0
replace PAUTH= 0.072 if vharm==1
replace PAUTH= 0.024 if vfair==1
replace PAUTH= 0.052 if vloyalty==1
replace PAUTH= 0.454 if vauthority==1
replace PAUTH= 0.012 if vsanctity==1

gen PSANC=0
replace PSANC= 0.100 if vharm==1
replace PSANC= 0.038 if vfair==1
replace PSANC= 0.047 if vloyalty==1
replace PSANC= 0.010 if vauthority==1
replace PSANC= 0.244 if vsanctity==1


gen PNONMORAL=0
replace PNONMORAL= 0.004 if vharm==1
replace PNONMORAL= 0.044 if vfair==1
replace PNONMORAL= 0.052 if vloyalty==1
replace PNONMORAL= 0.077 if vauthority==1
replace PNONMORAL= 0.024 if vsanctity==1

gen PCAREW = PCARE *Q44
gen PFAIRW = PFAIR *Q44
gen PLOYALW = PLOYAL *Q44
gen PAUTHW = PAUTH *Q44
gen PSANCW =PSANC *Q44
gen PNONMORALW =PNONMORAL *Q44

 
oprobit shame PCAREW PFAIRW PLOYALW PAUTHW PSANCW PNONMORALW  ncare nfair ningroup nauthority nsanctity gender Age white hispanic african bach postgrad republican democrat

oprobit contempt PCAREW PFAIRW PLOYALW PAUTHW PSANCW PNONMORALW   ncare nfair ningroup nauthority nsanctity gender Age white hispanic african bach postgrad republican democrat

oprobit angry PCAREW PFAIRW PLOYALW PAUTHW PSANCW PNONMORALW  ncare nfair ningroup nauthority nsanctity gender Age white hispanic african bach postgrad republican democrat

oprobit disgust  PCAREW PFAIRW PLOYALW PAUTHW PSANCW PNONMORALW  ncare nfair ningroup nauthority nsanctity gender Age white hispanic african bach postgrad republican democrat

oprobit elevation PCAREW PFAIRW PLOYALW PAUTHW PSANCW PNONMORALW   ncare nfair ningroup nauthority nsanctity gender Age white hispanic african bach postgrad republican democrat

oprobit pride PCAREW PFAIRW PLOYALW PAUTHW PSANCW PNONMORALW  ncare nfair ningroup nauthority nsanctity gender Age white hispanic african bach postgrad republican democrat

oprobit sympathy PCAREW PFAIRW PLOYALW PAUTHW PSANCW PNONMORALW  ncare nfair ningroup nauthority nsanctity gender Age white hispanic african bach postgrad republican democrat


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*Table A1.4: Voters’ Moral Emotional Responses to Politicians’ Moral Transgressions for Different Moral Foundations*******
*Table A1.4 in the OSM is the basis for Table 3 in the main manuscript**************************************************** 
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*Oprobit models per moral emotion displayed in Table A1.4 in OSM**********************************************************
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tab angry
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**interaction harmvignette and harm foundation
oprobit angry  i.vharm##c.ncare vfair vloyalty vauthority nfair ningroup nauthority nsanctity gender Age white hispanic african bach postgrad republican democrat
**interaction fair vignette and fair foundation
oprobit angry i.vfair##c.nfair vharm vloyalty vauthority ncare  ningroup nauthority nsanctity  gender Age white hispanic african bach postgrad republican democrat
**interaction loyalty vignette and loyalty foundation
oprobit angry vharm vfair i.vloyalty##c.ningroup vauthority ncare nfair  nauthority nsanctity gender Age white hispanic african bach postgrad republican democrat
*interaction authority vignette and authority foundation
oprobit angry vharm vfair vloyalty i.vauthority##c.nauthority ncare nfair ningroup nsanctity  gender Age white hispanic african bach postgrad republican democrat
*interaction sanctity vignette and sanctity foundation
oprobit angry vharm vfair vloyalty i.vsanctity##c.nsanctity ncare nfair ningroup nauthority   gender Age white hispanic african bach postgrad republican democrat
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tab contempt
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**interaction harmvignette and harm foundation
oprobit contempt i.vharm##c.ncare vfair vloyalty vauthority nfair ningroup nauthority nsanctity republican democrat  Age gender white hispanic african bach postgrad 
**interaction fair vignette and fair foundation
oprobit contempt i.vfair##c.nfair vharm vloyalty vauthority ncare  ningroup nauthority nsanctity  republican democrat  Age gender white hispanic african bach postgrad 
**interaction loyalty vignette and loyalty foundation
oprobit contempt vharm vfair i.vloyalty##c.ningroup vauthority ncare nfair  nauthority nsanctity republican democrat  Age gender white hispanic african bach postgrad 
*interaction authority vignette and authority foundation
oprobit contempt vharm vfair vloyalty i.vauthority##c.nauthority ncare nfair ningroup nsanctity republican democrat  Age gender white hispanic african bach postgrad 
*interaction sanctity vignette and sanctity foundation
oprobit contempt vharm vfair vloyalty i.vsanctity##c.nsanctity ncare nfair ningroup nauthority  republican democrat  Age gender white hispanic african bach postgrad 
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tab shame
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**interaction harmvignette and harm foundation
oprobit shame  i.vharm##c.ncare vfair vloyalty vauthority nfair ningroup nauthority nsanctity republican democrat  Age gender white hispanic african bach postgrad 
**interaction fair vignette and fair foundation
oprobit shame i.vfair##c.nfair vharm vloyalty vauthority ncare  ningroup nauthority nsanctity republican democrat  Age gender white hispanic african bach postgrad 
**interaction loyalty vignette and loyalty foundation
oprobit shame  vharm vfair i.vloyalty##c.ningroup vauthority ncare nfair  nauthority nsanctity republican democrat  Age gender white hispanic african bach postgrad 
*interaction authority vignette and authority foundation
oprobit shame vharm vfair vloyalty i.vauthority##c.nauthority ncare nfair ningroup nsanctity republican democrat  Age gender white hispanic african bach postgrad 
*interaction sanctity vignette and sanctity foundation
oprobit shame vharm vfair vloyalty i.vsanctity##c.nsanctity ncare nfair ningroup nauthority republican democrat   Age gender white hispanic african bach postgrad 
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tab disgust
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**interaction harmvignette and harm foundation
oprobit disgust  i.vharm##c.ncare vfair vloyalty vauthority nfair ningroup nauthority nsanctity republican democrat Age gender white hispanic african bach postgrad 
**interaction fair vignette and fair foundation
oprobit disgust i.vfair##c.nfair vharm vloyalty vauthority ncare  ningroup nauthority nsanctity  republican democrat Age gender white hispanic african bach postgrad 
**interaction loyalty vignette and loyalty foundation
oprobit disgust  vharm vfair i.vloyalty##c.ningroup vauthority ncare nfair  nauthority nsanctity republican democrat Age gender white hispanic african bach postgrad 
*interaction authority vignette and authority foundation
oprobit disgust vharm vfair vloyalty i.vauthority##c.nauthority ncare nfair ningroup nsanctity  republican democrat Age gender white hispanic african bach postgrad 
*interaction sanctity vignette and sanctity foundation
oprobit disgust vharm vfair vloyalty i.vsanctity##c.nsanctity ncare nfair ningroup nauthority republican democrat   Age gender white hispanic african bach postgrad 
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tab elevation 
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**interaction harmvignette and harm foundation
oprobit elevation  i.vharm##c.ncare vfair vloyalty vauthority nfair ningroup nauthority nsanctity republican democrat  Age gender  white hispanic african bach postgrad 
**interaction fair vignette and fair foundation
oprobit elevation i.vfair##c.nfair vharm vloyalty vauthority ncare  ningroup nauthority nsanctity republican democrat  Age gender white hispanic african bach postgrad 
**interaction loyalty vignette and loyalty foundation
oprobit elevation  vharm vfair i.vloyalty##c.ningroup vauthority ncare nfair  nauthority nsanctity republican democrat  Age gender white hispanic african bach postgrad 
*interaction authority vignette and authority foundation
oprobit elevation vharm vfair vloyalty i.vauthority##c.nauthority ncare nfair ningroup nsanctity republican democrat Age gender white hispanic african bach postgrad 
*interaction sanctity vignette and sanctity foundation
oprobit elevation vharm vfair vloyalty i.vsanctity##c.nsanctity ncare nfair ningroup nauthority republican democrat   Age gender white hispanic african bach postgrad 
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tab sympathy
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**interaction harmvignette and harm foundation
oprobit sympathy  i.vharm##c.ncare vfair vloyalty vauthority nfair ningroup nauthority nsanctity republican democrat  Age gender  white hispanic african bach postgrad 
**interaction fair vignette and fair foundation
oprobit sympathy i.vfair##c.nfair vharm vloyalty vauthority ncare  ningroup nauthority nsanctity republican democrat  Age gender white hispanic african bach postgrad 
**interaction loyalty vignette and loyalty foundation
oprobit sympathy  vharm vfair i.vloyalty##c.ningroup vauthority ncare nfair  nauthority nsanctity republican democrat  Age gender white hispanic african bach postgrad 
*interaction authority vignette and authority foundation
oprobit sympathy vharm vfair vloyalty i.vauthority##c.nauthority ncare nfair ningroup nsanctity republican democrat Age gender white hispanic african bach postgrad 
*interaction sanctity vignette and sanctity foundation
oprobit sympathy vharm vfair vloyalty i.vsanctity##c.nsanctity ncare nfair ningroup nauthority republican democrat   Age gender white hispanic african bach postgrad 
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tab pride
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**interaction harmvignette and harm foundation
oprobit pride i.vharm##c.ncare vfair vloyalty vauthority nfair ningroup nauthority nsanctity republican democrat  Age gender white hispanic african bach postgrad 
**interaction fair vignette and fair foundation
oprobit pride i.vfair##c.nfair vharm vloyalty vauthority ncare  ningroup nauthority nsanctity  republican democrat  Age gender white hispanic african bach postgrad 
**interaction loyalty vignette and loyalty foundation
oprobit pride vharm vfair i.vloyalty##c.ningroup vauthority ncare nfair  nauthority nsanctity republican democrat  Age gender white hispanic african bach postgrad 
*interaction authority vignette and authority foundation
oprobit pride vharm vfair vloyalty i.vauthority##c.nauthority ncare nfair ningroup nsanctity republican democrat  Age gender white hispanic african bach postgrad 
*interaction sanctity vignette and sanctity foundation
oprobit pride vharm vfair vloyalty i.vsanctity##c.nsanctity ncare nfair ningroup nauthority  republican democrat  Age gender white hispanic african bach postgrad 
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*Table 4 Voters’ Moral Emotional Responses to Politicians’ Moral Transgressions by Shared Partisanship in Manuscript******
*Table A1.5: Voters’ Emotional Responses to Politicians’ Moral Transgressions by Shared Partisanship in OSM***************
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*Table A1.6: Predicted Probabilities of Experiencing Specific Emotions when Exposed to In-Party and Out-Party Moral Violations 
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oprobit angry partisanship vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
margins, over(partisanship)
oprobit disgust partisanship vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
margins, over(partisanship)
oprobit contempt partisanship vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
margins, over(partisanship)
oprobit shame partisanship  vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
margins, over(partisanship)
oprobit pride partisanship vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
margins, over(partisanship)
oprobit elevation partisanship vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
recode elevation (min/1=1)(1.1/2=2)(2.1/3=3)(3.1/4=4)(4.1/max=5) , generate(elevation4)
oprobit elevation4 partisanship vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
margins, over(partisanship)
oprobit sympathy partisanship vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
margins, over(partisanship)
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*Table 5 Effect Strength of Party Id on Voters’ Moral Emotional Responses to Politicians’ Moral Transgressions in the manuscript
*Table A1.7: Effect of Party Id and Strength Party Id on Voters’ Moral Emotional Responses to Politicians’ Moral Transgressions
*Table A1.8: Predicted Probabilities of Experiencing Specific Emotions when Exposed to In-Party and Out-Party Moral Violations 
*Table A1.10: Predicted Probabilities of Experiencing Specific Emotions when Exposed to In-Party or Out-Party Moral Violations for Different Levels of Party Identity Strength  *************************************************************************************************
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*combined dem and party strength
gen Partystrength=.
replace Partystrength=1 if Q54==1
replace Partystrength=2 if Q54==2
replace Partystrength=3 if Q56==1
replace Partystrength=1 if Q55==1
replace Partystrength=2 if Q55==2
replace Partystrength=3 if Q56==2

label define partystrengthlabel2 1 "Strong" 2 "Weak" 3 "Leaning"
label values Partystrength partystrengthlabel2

drop if independent==1
drop if otherparty==1

oprobit angry i.partisanship##i.Partystrength vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
mtable, at(partisanship =(0 1))
mtable, at(Partystrength = (1 2 3))
mtable if partisanship==0,  at (Partystrength = (1 2 3))
mtable if partisanship==1, at (Partystrength = (1 2 3))
oprobit disgust partisanship##i.Partystrength vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
mtable, at(partisanship =(0 1))
mtable, at(Partystrength = (1 2 3))
mtable if partisanship==0,  at (Partystrength = (1 2 3))
mtable if partisanship==1, at (Partystrength = (1 2 3))
oprobit contempt partisanship##i.Partystrength vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
mtable, at(partisanship =(0 1))
mtable, at(Partystrength = (1 2 3))
mtable if partisanship==0,  at (Partystrength = (1 2 3))
mtable if partisanship==1, at (Partystrength = (1 2 3))
oprobit sympathy partisanship##i.Partystrength vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
mtable, at(partisanship =(0 1))
mtable, at(Partystrength = (1 2 3))
mtable if partisanship==0,  at (Partystrength = (1 2 3))
mtable if partisanship==1, at (Partystrength = (1 2 3))
oprobit shame partisanship##i.Partystrength  vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
mtable, at(partisanship =(0 1))
mtable, at(Partystrength = (1 2 3))
mtable if partisanship==0,  at (Partystrength = (1 2 3))
mtable if partisanship==1, at (Partystrength = (1 2 3))
oprobit pride partisanship##i.Partystrength vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
mtable, at(partisanship =(0 1))
mtable, at(Partystrength = (1 2 3))
mtable if partisanship==0,  at (Partystrength = (1 2 3))
mtable if partisanship==1, at (Partystrength = (1 2 3))
oprobit elevation partisanship##i.Partystrength vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
oprobit elevation4 partisanship##i.Partystrength vrepublican vdemocrat republican democrat Age gender  white hispanic african bach postgrad 
mtable, at(partisanship =(0 1))
mtable, at(Partystrength = (1 2 3))
mtable if partisanship==0,  at (Partystrength = (1 2 3))
mtable if partisanship==1, at (Partystrength = (1 2 3))
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